This thesis explores the possibility of using neural networks for solving the path
control problem, i.e. how to follow a predefined path as closely as possible. Two main
approaches are used to achieve this, namely supervised learning and reinforcement
learning. The supervised learning approach is based on existing path trackers which
are used to generate data for the training procedure. The reinforcement learning uses
a genetic algorithm and simulations to evaluate possible solutions. The supervised
learning controllers are constructed as feed forward neural networks only, while the
reinforcement learning controllers uses a recurrent neural network.
The results shows that neural networks can be trained to solve the path tracking
problem, both with supervised and reinforcement learning methods. Both the
feed forward networks and the recurrent networks outperform the geometric path
trackers. Further, a recurrent network was shown to perform better than a feed
forward network, which indicates that the dynamical properties of such networks
can be useful in path tracking applications.

BibTeX @mastersthesis{Insgård2018,author={Insgård, Viktor and Jansson, Lucas},title={Heavy vehicle path control with neural networks - Heavy vehicle path control with neural networks},abstract={This thesis explores the possibility of using neural networks for solving the path
control problem, i.e. how to follow a predefined path as closely as possible. Two main
approaches are used to achieve this, namely supervised learning and reinforcement
learning. The supervised learning approach is based on existing path trackers which
are used to generate data for the training procedure. The reinforcement learning uses
a genetic algorithm and simulations to evaluate possible solutions. The supervised
learning controllers are constructed as feed forward neural networks only, while the
reinforcement learning controllers uses a recurrent neural network.
The results shows that neural networks can be trained to solve the path tracking
problem, both with supervised and reinforcement learning methods. Both the
feed forward networks and the recurrent networks outperform the geometric path
trackers. Further, a recurrent network was shown to perform better than a feed
forward network, which indicates that the dynamical properties of such networks
can be useful in path tracking applications.},publisher={Institutionen för mekanik och maritima vetenskaper, Fordonsteknik och autonoma system, Chalmers tekniska högskola},place={Göteborg},year={2018},series={Master's thesis - Department of Mechanics and Maritime Sciences, no: 2018:27},keywords={ath control, neural networks, genetic algorithms, autonomous vehicle, heavy vehicle.},}

RefWorks RT GenericSR ElectronicID 255459A1 Insgård, ViktorA1 Jansson, LucasT1 Heavy vehicle path control with neural networks - Heavy vehicle path control with neural networksYR 2018AB This thesis explores the possibility of using neural networks for solving the path
control problem, i.e. how to follow a predefined path as closely as possible. Two main
approaches are used to achieve this, namely supervised learning and reinforcement
learning. The supervised learning approach is based on existing path trackers which
are used to generate data for the training procedure. The reinforcement learning uses
a genetic algorithm and simulations to evaluate possible solutions. The supervised
learning controllers are constructed as feed forward neural networks only, while the
reinforcement learning controllers uses a recurrent neural network.
The results shows that neural networks can be trained to solve the path tracking
problem, both with supervised and reinforcement learning methods. Both the
feed forward networks and the recurrent networks outperform the geometric path
trackers. Further, a recurrent network was shown to perform better than a feed
forward network, which indicates that the dynamical properties of such networks
can be useful in path tracking applications.PB Institutionen för mekanik och maritima vetenskaper, Fordonsteknik och autonoma system, Chalmers tekniska högskola,PB Institutionen för mekanik och maritima vetenskaper, Fordonsteknik och autonoma system, Chalmers tekniska högskola,T3 Master's thesis - Department of Mechanics and Maritime Sciences, no: 2018:27LA engLK http://publications.lib.chalmers.se/records/fulltext/255459/255459.pdfOL 30